1. Data Center Energy Efficiency: Scale-Up/Scale-Out Processor Design Background & Analysis
By Nick

2. Overview Data Centers Design Philosophies Comparisons Energy Usage
Minimize costs
Design Philosophies
Scale-up
Scale-out
Comparisons

3. Data Centers Originally in giant enterprises
Ran operations across buildings in business
Now available for rental by the common man
Webhosting
Small business rentals
Cloud Computing

4. Energy Usage It costs *a lot* to run a data center
Comparable to a small town’s energy usage
Can’t use constant power
Low power states
Dynamic power management
Traffic Engineering
Architecture designs…

5. Scale-Up Processors Improve control units to make more efficient
Upgrade/replace current machines with better versions
Tile Processors
Array of processing units
Networked together
Each computer becomes a “cluster”

6. Scale-Out Processors Add more units to existing system
Provides more workers
Less bottlenecks at top
Closer to the ideas of parallelization
Increased energy costs (more units)
Offset by increased throughput and energy efficiency

7. Analysis
Scale-out had higher performance efficiency compared to scale-up
High workload demand environments
However, too many units led to decrease in efficiency
Possibly due to “too many cooks in the kitchen”
Draining power without significant time decrease
Overhead constraints increase

8. Conclusion
Scale-out architectures seem to scale better as demand increases
However, there seems to be a limit with a constant workload
In those cases, scale-up provides further gains
Hybrid approach may be the best bet

9. References [1] https://www.gartner.com/it-glossary/data-center/
[2] Greenberg, A. et al. The Cost of a Cloud: Research Problems in Data Center Networks
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[5] Buyya, R., Jin, H., Cortes, T. Cluster Computing. Future Generations Computer Systems. Vol 18. Pages 5 –
[6] Taylor, M. Tiled Microprocessors. Massachusetts Institute of Technology. February 2007.
[7] Lotfi-Kamran, P. et al. Scale-Out Processors. Proceedings of the 39th Annual International Symposium on Computer Architecture. Pages 1 – 12. June 2012
[8] Niewiadomska-Szynkiewicz, E., et al. Dynamic power management in energy-aware computer networks and data intensive computing systems. Future Generation Computer Systems. Vol 37. Pages July 2014.
[9] Staff. The power of power-aware scheduling. Argonne National Library. January 28, 2014.
[10] Wenish, T. F., Buyuktosunoglu, A. Energy-Aware Computing. IEEE Computer Society, Page 6 - 8, September/October 2012.
[11] Dean, J. and Ghemawat, S. MapReduce: Simplified Data Processing on Large Clusters. Google, Inc.
[12] Banerjee, A. et al. Cooling-Aware and Thermal-Aware Workload Placement for Green HPC Data Centers. IMPACT Laboratory. Arizona State University.
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[14] Cunniffe, R. and Coghlan, B. Encouraging the Unexpected: Cluster Management for OS and Systems Research. Department of Computer Science. Trinity College. Dublin, Ireland.
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[16] Wang, Q., Shen, L. and Wang, Z. Research on Scale-Out Workloads and Optimal Design of Multicore Processors. Proceedings of International Conference on Soft Computing Techniques and Engineering Application. Pages December 21, 2013.